The invention generally relates to fault diagnosis and classification in large systems and more specifically to a method and system for diagnosing and classifying faults in a large system, wherein the components of the system are organized in a hierarchical interrelated manner.
A number of diagnostic models have been developed to identify, diagnose and classify faults in large systems, such as locomotives, aircraft engines, automobiles, turbines, computers and appliances. However, due to the complexity of such large systems, the use of an individual diagnostic model to isolate and classify faults may not provide an optimal evaluation of the performance of such large systems.
In order to overcome some of the challenges associated with the use of a single diagnostic model to isolate faults, a number of decision fusion techniques have been developed that combine model evidence from multiple diagnostic models, assure model compatibility and produce an accurate and efficient evaluation of the performance status of a system. Some of these techniques include the decision consensus approach and the most competent approach. The decision consensus approach accepts the decision of a majority of classifiers from each diagnostic model as the fused decision while the most competent approach accepts the decision of the most competent classifier. Another commonly used approach is to combine individual decisions from each diagnostic model within the decision fusion framework using weighted probabilities across all the classifiers and adjusting the associated weights based on historical performance, to optimize the overall diagnostic fusion decision. Other decision fusion techniques include layering information by weakening or strengthening individual decisions based on specific criteria.
One common characteristic of the existing decision fusion techniques is that a “flat” fault classification model is assumed. That is, all potential classification categories are considered as non-overlapping and independent. Therefore, subcomponent hierarchy interactions or subsystem hierarchy interactions within a system cannot be easily captured. In addition, the above decision fusion techniques lack the capability to integrate overlapped faults that are often observed from independently developed diagnostic models, especially when each diagnostic model includes information from heterogeneous information sources.
Several challenges exist in developing an effective decision fusion framework to diagnose large complex systems failures. Firstly, the development of individual decision models within the decision framework is generally based on diverse techniques and each decision model may provide different capability levels in terms of coverage and reliability. In addition, fault classification and granularities usually differ from one decision model to another and the results produced by the various decision models may even be contradictory. Finally, the information to be combined and considered from the various decision models may be highly heterogeneous in nature, including both categorical and continuous data.
Therefore, there is a need for the development of a hierarchical fault classification framework that is capable of providing decision fusion among largely diverse diagnostic models. In addition, there is a need for the development of a hierarchical fault classification framework that is capable of handling hierarchical subcomponent and subsystem interactions among the components of a large system.